The previous articles explained the theory of OEE metrics. Now we’ll cover how to successfully implement them in your manufacturing operation. Among other things, you’ll learn how to correctly capture OEE (overall equipment effectiveness) and leverage it to boost productivity. The process of introducing OEE can be summarized in four steps:
1. Choose a pilot machine
To gain some initial experience with OEE, begin by choosing a pilot machine. We recommend selecting the “process bottleneck”. A process bottleneck is a step in the manufacturing process that receives more work requests than it can handle, thus holding up the flow. Optimizing it will improve the overall process.
2. Make sure your personnel are appropriately trained
To successfully introduce OEE, you should make sure that the responsible team has a basic familiarity of how it works and its benefits. In addition to smoothing implementation, this will motivate your team to consistently take advantage of it for maximum results.
3. Capture data
In the following, we show you the data that you’ll need to calculate the OEE factors:
a) Number of good parts (good count): Good parts are parts that have no defects after the first pass and don’t need to be reworked. This number helps you calculate the quality (share of good parts out of the total). Good parts can be counted by hand or else automatically by counting digital signals sent by the machine.
b) Number of bad parts: Bad parts are parts that fall short of the required quality after the first pass. These parts can either be corrected by reworking them or disposed of. The sum of good and bad parts is equal to the number of all produced parts, which can be used to calculate performance and quality. Bad parts can be captured similarly to the good parts.
c) Ideal cycle time: This is the theoretical minimum time required to manufacture one part. Multiplying the ideal cycle time by the planned production time yields the number of parts that could be produced, which is needed to calculate performance. The ideal cycle time should be calculated realistically in order to also obtain realistic results. For example, if the cycle times are assumed to be too large, a machine efficiency greater than 100% can result.
d) Planned production time: The OEE metric doesn’t only consider losses that are caused during the planned production time. Consequently, it’s important for you to very carefully define the planned stops that will be left out of account, like for breaks, meetings etc. These play an important role for calculating both availability and performance.
e) Unplanned stops: Time periods during which your machines don’t produce as planned. The causes can include technical problems or a shortage of materials. The difference between planned production time and total unplanned stops is equal to actual production time, which reveals the availability loss. In order to manually capture these stops, you must capture the start and finish times of each one. Alternatively, you can leave this task to an automated system.
You’ll find more information on the topic of data capture for calculating OEE factors in our article “The benefits of automatic OEE capture”.
4. Calculate the OEE score and improve your processes
Now you have all the data that’s needed to very easily calculate the OEE factors and then the OEE metric itself according to the following formula:
As you already know by this time, OEE is a tool for monitoring machine efficiency. Please keep in mind, however, that simply capturing the OEE will not, by itself, enable you to produce more efficiently. In order to optimize your processes, you have to combine OEE with targeted measures to reduce the sources of losses in your manufacturing operation. Process optimization is a cyclic process in which the following three steps are repeated until you achieve your productivity goal.
1. Identify weaknesses: So that you can choose the right actions for improving the situation, first identify the factors that are holding productivity back. This requires you to regularly determine and analyze the OEE. The results reveal the causes of losses in the three OEE factors. For example, you can prepare a list of the most frequent causes of stops and quality defects and then systematically eliminate them one at a time.
2. Take action: Once it’s clear what is responsible for productivity losses, you can implement targeted measures to systematically eliminate the causes. These measures must be well documented, planned, and implemented.
3. Check effectiveness: Taking these steps isn’t enough, however. It’s also important to know whether the effort and expenditure have paid off. You should therefore be able to measure the effect of each individual measure as precisely as possible. This lets you concentrate on the most effective ones and reuse them if similar problems arise down the road. The positive effects of a measure can be reflected in a better OEE score, for example.
You’ll soon see the productivity of your machines increase. And this will inevitably lead you to realize that the OEE metric actually belongs in the hands of the production team. After you’ve achieved your initial successes and familiarized yourself with the OEE metric, you can then very easily scale up the same approach for a larger number of machines and boost productivity across your entire company.
Beneficial implementation of OEE calls for consistent, systematic collaboration by the entire production team. The data captured and the methods to capture it also play a central role in quantifying your OEE score. Last but not least, it takes a cyclical improvement process to achieve the desired level of productivity.
Do you have any questions or would you like support for introducing OEE? Our experts will be happy to help you.